Get Ahead with Expert-Led Training in Data Analytics Dashboard with Python

This course teaches you all of the skills to build interactive data analytics dashboards with Python. Specifically, you will be building a Coronavirus Forecasting Dashboard that shows historical and predicted values for deaths and cases for all countries in the world and US states from the ongoing coronavirus pandemic. The final product will be a live dashboard, automatically updated daily, hosted on a remote server for anyone, anywhere in the world to see.


Python Data Science Expert Instructor - Author of Multiple Books and Python LIbraries | Founder | Dunder Data

Teddy Petrou

Teddy Petrou is the author of Pandas Cookbook, a highly rated text on performing real-world data analysis with Pandas. He is also the author of the books Exercise Python and Master Data Analysis with Python. He is the founder of Dunder Data, a company that teaches the fundamentals of data science and machine learning. He really enjoys discovering best practices on how to use and teach data analysis with Python.

Learning Objectives

  • Learn how to complete a comprehensive, end-to-end project in Python using a vast array of skills

  • Build an interactive data analytics dashboard using the Dash library in Python

  • Model coronavirus cases and deaths using generalized logistic functions

  • Smooth data using locally weighted scatterplot smoothing

  • Learn how to use Plotly, an interactive data visualization library in Python targeting the web

  • Encapsulate all of your code into Python classes to ease automation

  • Setup an Ubuntu server running NGINX to host the dashboard on the web for all to see

Build an Interactive Data Analytics Dashboard with Python Part 1

Module 1: Getting Started

  • Creating the virtual environment
  • Launching and exploring the dashboard
  • Opening the Jupyter Notebooks


Module 2: Getting, Cleaning, and Transforming the Data

  • Downloading the data
  • Finding and handling bad data


Module 3: Data Smoothing

  • Moving average smoothing
  • LOWESS smoothing

Build an Interactive Data Analytics Dashboard with Python Part 2

Module 4: Exponential Growth and Decline Models

  • Exponential growth and decline functions
  • Finding optimal parameters
  • Predicting unseen data
  • Automating model training


Module 5: Logistic Growth Models

  • Introduction to S-curves
  • Estimating logistic function parameters
  • Generalized logistic function


Module 6: Modeling New Waves

  • Limiting the data
  • Shifting the data
  • Automating parameter bounds


Module 7: Encapsulation into Classes

  • Building a single class to model cases
  • Predicting deaths using case fatality ratio
  • Creating final dashboard tables

Build an Interactive Data Analytics Dashboard with Python Part 3

Module 8: Visualizations with Plotly

  • Discovering trace properties
  • Updating the layout
  • Automating plotting of each area
  • Choropleth maps


Module 9: Building the Dashboard with Dash

  • Parts of a Dash application
  • Creating a Data Table
  • Adding maps to the dashboard
  • Interactivity using callbacks
  • Putting all components and interactivity together


Module 10: Deployment

  • Launching the dashboard on Python Anywhere
  • Launching the dashboard on your own Ubuntu server
  • Automatic daily updates

Background knowledge

  • Fundamental knowledge of the Python programming language and the Pandas library is necessary

  • Previous work with Jupyter Notebooks would be helpful as all material is delivered with them

Target Audience

  • All data enthusiasts and professionals that wish to have the capability of creating and launching their own interactive analytics dashboards using Python.

  • Who would like to learn all of the steps to complete a comprehensive project

  • Who likes to use many different technologies together in a single project

  • Who enjoys building data visualizations and predictive models

  • Who would like to learn how to deploy their work on remote web servers

  • Who enjoys completing exercises to test knowledge learned